import streamlit as st
from dotenv import load_dotenv
from htmlTemplates import css, bot_template, user_template
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFaceHub
from PyPDF2 import PdfReader
# from langchain.document_loaders import PyPDFLoader
# loader = PyPDFLoader("/content/drive/MyDrive/data_sets/ramayana.pdf")


def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text


def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


def get_vectorstore(text_chunks):
    # embeddings = OpenAIEmbeddings
    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", model_kwargs={"device": "cpu"})
    # vectorstore = FAISS.from_text(text=text_chunks, embedding=embeddings)
    # vectorstore = FAISS.from_documents(text=text_chunks, embedding=embeddings)
    # vectorstore = FAISS.from_documents(text_chunks, embeddings)
    vectorstore = FAISS.from_texts(text_chunks, embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    # llm = ChatOpenAI()
    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain


def handle_userinput(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(user_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)
        else:
            st.write(bot_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)


# def main():
#     load_dotenv()
#     st.set_page_config(page_title="Chat with PDFs", page_icon=":books:")
#
#     if "conversation" not in st.session_state:
#         st.session_state.conversation = None
#
#     if "chat_history" not in st.session_state:
#         st.session_state.chat_history = None
#
#     st.header("Chat with Multiple PDFs :books:")
#     user_question = st.text_input("Ask a question about your documents:")
#     if user_question:
#         handle_user_input(user_question)
#
#     with st.sidebar:
#         st.subheader("Your documents")
#         pdf_docs = st.file_uploader(
#             "Upload you PDFs here and click on 'Process'", accept_multiple_files=True)
#         if st.button("Process"):
#             with st.spinner("Processing"):
#                 # get pdf text
#                 raw_text = get_pdf_text(pdf_docs)
#
#                 # get the text chunks
#                 text_chunks = get_text_chunks(raw_text)
#
#                 # create vector store
#                 vectorstore = get_vectorstore(text_chunks)
#
#                 # create conversation chain
#                 st.session_stste.conversation = get_conversation_chain(vectorstore)
#
#     # Display on the user interface
#     # if st.session_state:
#     #
#     #     for i in range(len(st.session_state['chatgpt_reply']) - 1, -1, -1):
#     #         message(st.session_state['user_reply'][i], is_user=True, key=str(i) + '_user')
#     #         message(st.session_state["chatgpt_reply"][i], key=str(i))

def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple PDFs",
                       page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with multiple PDFs :books:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                raw_text = get_pdf_text(pdf_docs)

                # get the text chunks
                text_chunks = get_text_chunks(raw_text)

                # create vector store
                vectorstore = get_vectorstore(text_chunks)

                # create conversation chain
                st.session_state.conversation = get_conversation_chain(
                    vectorstore)


if __name__ == '__main__':
    main()
